Basic Setup
── Attaching packages ──────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
✓ ggplot2 3.3.2 ✓ purrr 0.3.4
✓ tibble 3.0.3 ✓ dplyr 1.0.2
✓ tidyr 1.1.2 ✓ stringr 1.4.0
✓ readr 1.3.1 ✓ forcats 0.5.0
── Conflicts ─────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()
Loading required package: lattice
Loading required package: ggformula
Loading required package: ggstance
Attaching package: 'ggstance'
The following objects are masked from 'package:ggplot2':
geom_errorbarh, GeomErrorbarh
New to ggformula? Try the tutorials:
learnr::run_tutorial("introduction", package = "ggformula")
learnr::run_tutorial("refining", package = "ggformula")
Loading required package: mosaicData
Loading required package: Matrix
Attaching package: 'Matrix'
The following objects are masked from 'package:tidyr':
expand, pack, unpack
Registered S3 method overwritten by 'mosaic':
method from
fortify.SpatialPolygonsDataFrame ggplot2
The 'mosaic' package masks several functions from core packages in order to add
additional features. The original behavior of these functions should not be affected by this.
Note: If you use the Matrix package, be sure to load it BEFORE loading mosaic.
Have you tried the ggformula package for your plots?
Attaching package: 'mosaic'
The following object is masked from 'package:Matrix':
mean
The following objects are masked from 'package:dplyr':
count, do, tally
The following object is masked from 'package:purrr':
cross
The following object is masked from 'package:ggplot2':
stat
The following objects are masked from 'package:stats':
binom.test, cor, cor.test, cov, fivenum, IQR, median, prop.test,
quantile, sd, t.test, var
The following objects are masked from 'package:base':
max, mean, min, prod, range, sample, sum
Attaching package: 'scales'
The following object is masked from 'package:mosaic':
rescale
The following object is masked from 'package:purrr':
discard
The following object is masked from 'package:readr':
col_factor
Loading required package: knitr
Import Colors
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
change = col_double(),
responses = col_double(),
time = col_double(),
prev_condition = col_double(),
prev_change = col_double(),
color = col_double()
)
`summarise()` regrouping output by 'subject', 'age' (override with `.groups` argument)
Import Objects
Warning in rm(objectList): object 'objectList' not found
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Duplicated column names deduplicated: 'time' => 'time_1' [9]
Parsed with column specification:
cols(
`#subject` = col_double(),
block = col_double(),
trial = col_double(),
condition = col_double(),
setsize = col_double(),
change = col_double(),
time = col_double(),
responses = col_double(),
time_1 = col_double(),
Pic = col_character(),
prev_condition = col_double(),
prev_setsize = col_double(),
prev_change = col_double(),
prev_time = col_double()
)
Warning: Problem with `mutate()` input `pic_num`.
ℹ NAs introduced by coercion
ℹ Input `pic_num` is `as.numeric(Pic)`.
Warning in mask$eval_all_mutate(dots[[i]]): NAs introduced by coercion
`summarise()` regrouping output by 'subject', 'age' (override with `.groups` argument)
`summarise()` regrouping output by 'subject', 'age', 'setsize' (override with `.groups` argument)
`summarise()` regrouping output by 'subject' (override with `.groups` argument)
`summarise()` ungrouping output (override with `.groups` argument)
`summarise()` ungrouping output (override with `.groups` argument)
`summarise()` ungrouping output (override with `.groups` argument)
join color & objects
colorListResult %>%ungroup() %>% select(age,condition,mean_answer_correct,mean_response_time,kPashler,k,joinKey) -> ColorJoin
names(ColorJoin) <- c("age_c","condition_c","mean_answer_correct_c","mean_response_time_c","kPashler_c","k_c","joinKey")
objectResult %>% inner_join(ColorJoin,by = "joinKey") %>%
mutate(diffAnswerRate = mean_answer_correct-mean_answer_correct_c)%>%
mutate(diffK = k-k_c)-> testsJoined
objectResultCondition %>% inner_join(ColorJoin,by = "joinKey") -> testConditionJoined
colorListResult %>% ungroup() %>% select(subject,age,condition,mean_answer_correct,mean_response_time,kPashler,k) %>% mutate(test="color") %>% rename(setsize = condition) -> ColorJoinLong
objectResult %>% ungroup() %>% select(subject,age,setsize,mean_answer_correct,mean_response_time,kPashler,k) %>% mutate(test="object") -> ObjectJoinLong
joinedTestList = rbind(ObjectJoinLong,ColorJoinLong)
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Erster Schritt Überblick der Gesamtanzahl der falschen und richtigen Antworten
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Gib eine Tabelle mit den zusammenfassenden Daten für Color & Objects aus
colorList %>% group_by(condition,age) %>% summarise(Gesamt = n(),Richtig = sum(answer_correct == 1),Falsch = sum(answer_correct == 0), ProzentRichtig = scales::percent(round(mean(answer_correct == 1),2)),ProzentFalsch = scales::percent(round(mean(answer_correct == 0),2))) %>% rename(SetSize = condition) %>% kable(caption = "Überblick Versuchsreihe Color",align = "llcccrr")
`summarise()` regrouping output by 'condition' (override with `.groups` argument)
| SetSize | age | Gesamt | Richtig | Falsch | ProzentRichtig | ProzentFalsch |
|---|---|---|---|---|---|---|
| 2 | old | 1297 | 1226 | 71 | 95% | 5% |
| 2 | young | 1298 | 1268 | 30 | 98% | 2% |
| 4 | old | 1297 | 945 | 352 | 73% | 27% |
| 4 | young | 1303 | 1139 | 164 | 87% | 13% |
| 6 | old | 1291 | 863 | 428 | 67% | 33% |
| 6 | young | 1295 | 946 | 349 | 73% | 27% |
| 8 | old | 1315 | 782 | 533 | 59% | 41% |
| 8 | young | 1304 | 838 | 466 | 64% | 36% |
objectList %>% group_by(setsize,age) %>% summarise(Gesamt = n(),Richtig = sum(answer_correct == 1),Falsch = sum(answer_correct == 0), ProzentRichtig = scales::percent(round(mean(answer_correct == 1),2)),ProzentFalsch = scales::percent(round(mean(answer_correct == 0),2))) %>% kable(caption = "Überblick Versuchsreihe Real world objects",align = "llcccrr")
`summarise()` regrouping output by 'setsize' (override with `.groups` argument)
| setsize | age | Gesamt | Richtig | Falsch | ProzentRichtig | ProzentFalsch |
|---|---|---|---|---|---|---|
| 2 | old | 3840 | 3539 | 301 | 92% | 8% |
| 2 | young | 3840 | 3607 | 233 | 94% | 6% |
| 4 | old | 3840 | 3066 | 774 | 80% | 20% |
| 4 | young | 3840 | 3249 | 591 | 85% | 15% |
| 6 | old | 3840 | 2678 | 1162 | 70% | 30% |
| 6 | young | 3840 | 2911 | 929 | 76% | 24% |
#colorListResult %>% ungroup() %>% group_by(age,condition) %>% summarise(mean(hitRate),mean(falseAlarmRate),mean(k),mean(kPashler))%>% kable(caption = "Überblick Versuchsreihe Color objects K",align = "llcccrr")
Plotte einen Überblick der Gesamtmenge
colorList %>% mutate(age=factor(age, levels = c("young","old")),
answer_correct=factor(answer_correct, levels = c(0,1),labels = c("False","Correct"))) %>%
group_by(age,condition,answer_correct) %>% summarise(N=n()) %>% ungroup() %>%
group_by(age,condition) %>%
mutate(Total=sum(N),Percent=N/Total,
Lab=paste0(N,' (',paste0(round(100*Percent,0),'%'),')')) -> SumsColors
`summarise()` regrouping output by 'age', 'condition' (override with `.groups` argument)
#Plot
ggplot(SumsColors,aes(x=age,y=N,fill=answer_correct))+
geom_bar(stat='identity',position = position_stack())+
facet_wrap(.~condition,scales = 'free')+
geom_text(aes(label=Lab),position = position_stack(vjust = .5),size=3)+
geom_text(aes(y=Total,label=Total),vjust=-0.25,size=3)+
labs(title="Colors",subtitle = "per setsize grouped by age", x="age", y="# answer correct", fill="Answer")+scale_fill_brewer(palette = "Pastel1")+
ylim(0, 1400)
objectList %>% mutate(age=factor(age, levels = c("young","old")),
answer_correct=factor(answer_correct, levels = c(0,1),labels = c("False","Correct"))) %>%
group_by(age,setsize,answer_correct) %>% summarise(N=n()) %>% ungroup() %>%
group_by(age,setsize) %>%
mutate(Total=sum(N),Percent=N/Total,
Lab=paste0(N,' (',paste0(round(100*Percent,0),'%'),')')) -> SumsObjects
`summarise()` regrouping output by 'age', 'setsize' (override with `.groups` argument)
#Plot
ggplot(SumsObjects,aes(x=age,y=N,fill=answer_correct))+
geom_bar(stat='identity',position = position_stack())+
facet_wrap(.~setsize,scales = 'free')+
geom_text(aes(label=Lab),position = position_stack(vjust = .5),size=3)+
geom_text(aes(y=Total,label=Total),vjust=-0.25,size=3)+
labs(title="Real world objects",subtitle = "per setsize grouped by age", x="age", y="# answer correct", fill="Answer")+scale_fill_brewer(palette = "Pastel1")
Plotte die Streuung von Colors und real objects
colorListResult %>% ggplot(mapping=aes(x=age, y=mean_answer_correct,fill=age)) + geom_violin() + geom_jitter(width = 0.2, alpha = 0.6)+scale_y_continuous(labels = scales::percent)+labs(title="Colors",subtitle = "distribution test persons mean value of correct answers", x="age", y="mean answers correct", fill="Answer")
objectResult %>% ggplot(mapping=aes(x=age, y=mean_answer_correct,fill=age)) + geom_violin() + geom_jitter(width = 0.2, alpha = 0.6)+scale_y_continuous(labels = scales::percent)+labs(title="Real world objects",subtitle = "distribution test persons mean value of correct answers", x="age", y="mean answers correct", fill="Answer")
colorListResult %>% ggplot(mapping=aes(x=age, y=mean_answer_correct,fill=age))+ geom_violin() + geom_jitter(width = 0.2, alpha = 0.6)+facet_wrap(.~condition,scales = 'free')+scale_y_continuous(labels = scales::percent)+labs(title="Colors",subtitle = "distribution test persons mean value of correct answers", x="age", y="mean answers correct", fill="Answer")
objectResult %>% ggplot(mapping=aes(x=age, y=mean_answer_correct,fill=age))+ geom_violin() + geom_jitter(width = 0.2, alpha = 0.6)+facet_wrap(.~setsize,scales = 'free')+scale_y_continuous(labels = scales::percent)+labs(title="Real world objects",subtitle = "distribution test persons mean value of correct answers", x="age", y="mean answers correct", fill="Answer")
colorListResult %>% ggplot(mapping=aes(x=age, y=mean_answer_correct,fill=age)) + geom_boxplot() + geom_jitter(width = 0.2, alpha = 0.6)+scale_y_continuous(labels = scales::percent)+labs(title="Colors",subtitle = "distribution test persons mean value of correct answers", x="age", y="mean answers correct", fill="Answer")
objectResult %>% ggplot(mapping=aes(x=age, y=mean_answer_correct,fill=age)) + geom_boxplot() + geom_jitter(width = 0.2, alpha = 0.6)+scale_y_continuous(labels = scales::percent)+labs(title="Real world objects",subtitle = "distribution test persons mean value of correct answers", x="age", y="mean answers correct", fill="Answer")
colorListResult %>% ggplot(mapping=aes(x=age, y=mean_answer_correct,fill=age))+ geom_boxplot() + geom_jitter(width = 0.2, alpha = 0.6)+facet_wrap(.~condition,scales = 'free')+scale_y_continuous(labels = scales::percent)+labs(title="Colors",subtitle = "distribution test persons mean value of correct answers", x="age", y="mean answers correct", fill="Answer")
objectResult %>% ggplot(mapping=aes(x=age, y=mean_answer_correct,fill=age))+ geom_boxplot() + geom_jitter(width = 0.2, alpha = 0.6)+facet_wrap(.~setsize,scales = 'free')+scale_y_continuous(labels = scales::percent)+labs(title="Real world objects",subtitle = "distribution test persons mean value of correct answers", x="age", y="mean answers correct", fill="Answer")
Häufigkeitsverteilung
colorListResult %>% ggplot(mapping=aes(x=mean_answer_correct, fill=age))+ geom_histogram(bins = 20)+labs(title="Colors",subtitle = "Häufigkeiten korrekter Antworten", x="Mean answers correct")+scale_x_continuous(labels = scales::percent)
colorListResult %>% ggplot(mapping=aes(x=mean_answer_correct, fill=age))+ geom_histogram(bins=20)+facet_wrap(.~condition,scales = 'free')+scale_x_continuous(labels = scales::percent)+labs(title="Colors",subtitle = "Häufigkeiten korrekter Antworten", x="Mean answers correct")
objectResult %>% ggplot(mapping=aes(x=mean_answer_correct, fill=age))+ geom_histogram(bins=20)+labs(title="Real world objects",subtitle = "Häufigkeiten korrekter Antworten", x="Mean answers correct")+scale_x_continuous(labels = scales::percent)
objectResult %>% ggplot(mapping=aes(x=mean_answer_correct, fill=age))+ geom_histogram(bins=20)+facet_wrap(.~setsize,scales = 'free')+scale_x_continuous(labels = scales::percent)+labs(title="Real world objects",subtitle = "Häufigkeiten korrekter Antworten", x="Mean answers correct")
colorListResult %>% ggplot(mapping=aes(x=mean_answer_correct, fill=age))+ geom_density (alpha=0.2)+labs(title="Colors",subtitle = "Häufigkeiten korrekter Antworten", x="Mean answers correct")+scale_x_continuous(labels = scales::percent)
colorListResult %>% ggplot(mapping=aes(x=mean_answer_correct, fill=age))+ geom_density( alpha=0.2)+facet_wrap(.~condition,scales = 'free')+scale_x_continuous(labels = scales::percent)+labs(title="Colors",subtitle = "Häufigkeiten korrekter Antworten", x="Mean answers correct")
objectResult %>% ggplot(mapping=aes(x=mean_answer_correct, fill=age))+ geom_density(alpha=0.2)+labs(title="Real world objects",subtitle = "Häufigkeiten korrekter Antworten", x="Mean answers correct")+scale_x_continuous(labels = scales::percent)
objectResult %>% ggplot(mapping=aes(x=mean_answer_correct, fill=age))+ geom_density(alpha=0.2)+facet_wrap(.~setsize,scales = 'free')+scale_x_continuous(labels = scales::percent)+labs(title="Real world objects",subtitle = "Häufigkeiten korrekter Antworten", x="Mean answers correct")
Vergleich Color vs. Objects gleiche Altersgruppen
joinedTestList %>% ggplot(mapping=aes(x=mean_answer_correct, fill=test))+ geom_density (alpha=0.2)+labs(title="Colors vs Object",subtitle = "Häufigkeiten korrekter Antworten", x="Mean answers correct")+scale_x_continuous(labels = scales::percent)
joinedTestList %>% ggplot(mapping=aes(x=mean_answer_correct, fill=test))+ geom_density (alpha=0.2)+facet_wrap(.~setsize,scales = 'free')+labs(title="Colors vs Object",subtitle = "Häufigkeiten korrekter Antworten per Set Größe", x="Mean answers correct")+scale_x_continuous(labels = scales::percent)
Korrelation
colorListResult %>% ggplot(aes(x=mean_answer_correct, y=mean_response_time,color = age))+geom_point()+geom_smooth(method = lm, se = FALSE)
`geom_smooth()` using formula 'y ~ x'
colorListResult %>% ggplot(aes(x=mean_answer_correct, y=mean_response_time,color = age))+geom_point()+geom_smooth(method = "loess", span = 0.15, method.args = list(degree=1))
`geom_smooth()` using formula 'y ~ x'
colorListResult %>% ggplot(aes(x=mean_answer_correct, y=mean_response_time,color = age))+geom_point()+geom_smooth(method = lm, se = FALSE)+facet_wrap(.~condition,scales = 'free')
`geom_smooth()` using formula 'y ~ x'
objectResult %>% ggplot(aes(x=mean_answer_correct, y=mean_response_time,color = age))+geom_point()+geom_smooth(method = lm, se = FALSE)
`geom_smooth()` using formula 'y ~ x'
objectResult %>% ggplot(aes(x=mean_answer_correct, y=mean_response_time,color = age))+geom_point()+geom_smooth(method = lm, se = FALSE)+facet_wrap(.~setsize,scales = 'free')
`geom_smooth()` using formula 'y ~ x'
objectResult %>% filter(age==“young”,setsize==6) %>% ungroup %>% select(mean_answer_correct) -> testIt shapiro.test(testIt$mean_answer_correct)
Normalverteilung:
colorListResult %>% filter(condition==2,age=="old") -> df_means_o2
fromTo <- round(range(df_means_o2$mean_answer_correct),2)+c(-0.08,0.08)
limits <- seq(from=fromTo[1], to=fromTo[2], by=0.02)
hist(df_means_o2$mean_answer_correct,freq=FALSE,xlim = fromTo ,xlab = "Mittelwert korrekte Antworten",ylab = "relative Häufigkeit",breaks=limits,main = "Histogramm & Normalverteilung für Old, Setsize 2")
rug(jitter(df_means_o2$mean_answer_correct))
curve(dnorm(x,mean(df_means_o2$mean_answer_correct),sd(df_means_o2$mean_answer_correct)),lwd=2,col="blue",add=TRUE)
colorListResult %>% filter(condition==4,age=="old") -> df_means_o4
fromTo <- round(range(df_means_o4$mean_answer_correct),2)+c(-0.08,0.08)
limits <- seq(from=fromTo[1], to=fromTo[2], by=0.02)
hist(df_means_o4$mean_answer_correct,freq=FALSE,xlim = fromTo ,xlab = "Mittelwert korrekte Antworten",ylab = "relative Häufigkeit",breaks=limits,main = "Histogramm & Normalverteilung für Old, Setsize 4")
rug(jitter(df_means_o4$mean_answer_correct))
curve(dnorm(x,mean(df_means_o4$mean_answer_correct),sd(df_means_o4$mean_answer_correct)),lwd=2,col="blue",add=TRUE)
colorListResult %>% filter(condition==6,age=="old") -> df_means_o6
fromTo <- round(range(df_means_o6$mean_answer_correct),2)+c(-0.08,0.08)
limits <- seq(from=fromTo[1], to=fromTo[2], by=0.02)
hist(df_means_o6$mean_answer_correct,freq=FALSE,xlim = fromTo ,xlab = "Mittelwert korrekte Antworten",ylab = "relative Häufigkeit",breaks=limits,main = "Histogramm & Normalverteilung für Old, Setsize 6")
rug(jitter(df_means_o6$mean_answer_correct))
curve(dnorm(x,mean(df_means_o6$mean_answer_correct),sd(df_means_o6$mean_answer_correct)),lwd=2,col="blue",add=TRUE)
colorListResult %>% filter(condition==8,age=="old") -> df_means_o8
fromTo <- round(range(df_means_o8$mean_answer_correct),2)+c(-0.08,0.08)
limits <- seq(from=fromTo[1], to=fromTo[2], by=0.02)
hist(df_means_o8$mean_answer_correct,freq=FALSE,xlim = fromTo ,xlab = "Mittelwert korrekte Antworten",ylab = "relative Häufigkeit",breaks=limits,main = "Histogramm & Normalverteilung für Old, Setsize 8")
rug(jitter(df_means_o8$mean_answer_correct))
curve(dnorm(x,mean(df_means_o8$mean_answer_correct),sd(df_means_o8$mean_answer_correct)),lwd=2,col="blue",add=TRUE)
colorListResult %>% filter(condition==2,age=="young") -> df_means_y2
fromTo <- round(range(df_means_y2$mean_answer_correct),2)+c(-0.08,0.08)
limits <- seq(from=fromTo[1], to=fromTo[2], by=0.02)
hist(df_means_y2$mean_answer_correct,freq=FALSE,xlim = fromTo ,xlab = "Mittelwert korrekte Antworten",ylab = "relative Häufigkeit",breaks=limits,main = "Histogramm & Normalverteilung für Young, Setsize 2")
rug(jitter(df_means_y2$mean_answer_correct))
curve(dnorm(x,mean(df_means_y2$mean_answer_correct),sd(df_means_y2$mean_answer_correct)),lwd=2,col="blue",add=TRUE)
colorListResult %>% filter(condition==4,age=="young") -> df_means_y4
fromTo <- round(range(df_means_y4$mean_answer_correct),2)+c(-0.08,0.08)
limits <- seq(from=fromTo[1], to=fromTo[2], by=0.02)
hist(df_means_y4$mean_answer_correct,freq=FALSE,xlim = fromTo ,xlab = "Mittelwert korrekte Antworten",ylab = "relative Häufigkeit",breaks=limits,main = "Histogramm & Normalverteilung für Young, Setsize 4")
rug(jitter(df_means_y4$mean_answer_correct))
curve(dnorm(x,mean(df_means_y4$mean_answer_correct),sd(df_means_y4$mean_answer_correct)),lwd=2,col="blue",add=TRUE)
colorListResult %>% filter(condition==6,age=="young") -> df_means_y6
fromTo <- round(range(df_means_y6$mean_answer_correct),2)+c(-0.08,0.08)
limits <- seq(from=fromTo[1], to=fromTo[2], by=0.02)
hist(df_means_y6$mean_answer_correct,freq=FALSE,xlim = fromTo ,xlab = "Mittelwert korrekte Antworten",ylab = "relative Häufigkeit",breaks=limits,main = "Histogramm & Normalverteilung für Young, Setsize 6")
rug(jitter(df_means_y6$mean_answer_correct))
curve(dnorm(x,mean(df_means_y6$mean_answer_correct),sd(df_means_y6$mean_answer_correct)),lwd=2,col="blue",add=TRUE)
colorListResult %>% filter(condition==8,age=="young") -> df_means_y8
fromTo <- round(range(df_means_o8$mean_answer_correct),2)+c(-0.08,0.08)
limits <- seq(from=fromTo[1], to=fromTo[2], by=0.02)
hist(df_means_y8$mean_answer_correct,freq=FALSE,xlim = fromTo ,xlab = "Mittelwert korrekte Antworten",ylab = "relative Häufigkeit",breaks=limits,main = "Histogramm & Normalverteilung für Young, Setsize 8")
rug(jitter(df_means_y8$mean_answer_correct))
curve(dnorm(x,mean(df_means_y8$mean_answer_correct),sd(df_means_y8$mean_answer_correct)),lwd=2,col="blue",add=TRUE)
K Wert
# Color List
colorListResult %>% ungroup() %>% group_by(age,condition) %>%summarise(round(mean(hitRate),2),round(sd(hitRate),2),round(mean(falseAlarmRate),2),round(sd(falseAlarmRate),2),round(mean(k),2),round(sd(k),2),round(mean(kPashler),2),round(sd(kPashler),2)) ->df_KStat_data
`summarise()` regrouping output by 'age' (override with `.groups` argument)
names(df_KStat_data) <- c("age","setsize","mean_hit_rate","sd_hit_rate","mean_false_alarm","sd_false_alarm","mean_k","sd_k","mean_kPashler","sd_kPashler")
colorListResult %>% ungroup() %>% group_by(age,condition) %>%summarise(paste(round(mean(hitRate),2),"/",round(sd(hitRate),2)),paste(round(mean(falseAlarmRate),2),"/",round(sd(falseAlarmRate),2)),paste(round(mean(k),2),"/",round(sd(k),2)),paste(round(mean(kPashler),2),"/",round(sd(kPashler),2))) ->df_KStat
`summarise()` regrouping output by 'age' (override with `.groups` argument)
names(df_KStat) <- c("age","Set Size","HitRate Mean/SD","False Alarm Mean/SD","k Mean (SD)","kPashler MEAN/SD")
df_KStat %>% kable(caption = "Überblick Versuchsreihe Color objects K",align = "lcccccr")
| age | Set Size | HitRate Mean/SD | False Alarm Mean/SD | k Mean (SD) | kPashler MEAN/SD |
|---|---|---|---|---|---|
| old | 2 | 0.95 / 0.05 | 0.06 / 0.05 | 1.78 / 0.15 | 1.88 / 0.11 |
| old | 4 | 0.82 / 0.11 | 0.37 / 0.17 | 1.83 / 0.69 | 2.85 / 0.78 |
| old | 6 | 0.8 / 0.16 | 0.47 / 0.2 | 2.02 / 0.71 | 4.09 / 1.28 |
| old | 8 | 0.77 / 0.16 | 0.58 / 0.18 | 1.51 / 0.82 | 3.77 / 2.19 |
| young | 2 | 0.99 / 0.02 | 0.03 / 0.03 | 1.91 / 0.07 | 1.97 / 0.04 |
| young | 4 | 0.95 / 0.03 | 0.21 / 0.11 | 2.99 / 0.43 | 3.77 / 0.17 |
| young | 6 | 0.89 / 0.07 | 0.43 / 0.16 | 2.76 / 1 | 4.72 / 0.9 |
| young | 8 | 0.87 / 0.07 | 0.58 / 0.12 | 2.27 / 0.72 | 5.52 / 0.98 |
# Object List
objectResult %>% ungroup() %>% group_by(age,setsize) %>%summarise(round(mean(hitRate),2),round(sd(hitRate),2),round(mean(falseAlarmRate),2),round(sd(falseAlarmRate),2),round(mean(k),2),round(sd(k),2),round(mean(kPashler),2),round(sd(kPashler),2)) ->df_O_KStat_data
`summarise()` regrouping output by 'age' (override with `.groups` argument)
names(df_O_KStat_data) <- c("age","setsize","mean_hit_rate","sd_hit_rate","mean_false_alarm","sd_false_alarm","mean_k","sd_k","mean_kPashler","sd_kPashler")
objectResult %>% ungroup() %>% group_by(age,setsize) %>%summarise(paste(round(mean(hitRate),2),"/",round(sd(hitRate),2)),paste(round(mean(falseAlarmRate),2),"/",round(sd(falseAlarmRate),2)),paste(round(mean(k),2),"/",round(sd(k),2)),paste(round(mean(kPashler),2),"/",round(sd(kPashler),2))) ->df_O_KStat
`summarise()` regrouping output by 'age' (override with `.groups` argument)
names(df_O_KStat) <- c("age","Set Size","HitRate Mean/SD","False Alarm Mean/SD","k Mean (SD)","kPashler MEAN/SD")
df_O_KStat %>% kable(caption = "Überblick Versuchsreihe Real World Objects K",align = "lcccccr")
| age | Set Size | HitRate Mean/SD | False Alarm Mean/SD | k Mean (SD) | kPashler MEAN/SD |
|---|---|---|---|---|---|
| old | 2 | 0.87 / 0.07 | 0.03 / 0.03 | 1.69 / 0.13 | 1.74 / 0.13 |
| old | 4 | 0.77 / 0.07 | 0.17 / 0.07 | 2.39 / 0.27 | 2.89 / 0.31 |
| old | 6 | 0.75 / 0.12 | 0.36 / 0.13 | 2.37 / 0.46 | 3.76 / 0.85 |
| young | 2 | 0.92 / 0.04 | 0.04 / 0.03 | 1.76 / 0.1 | 1.82 / 0.09 |
| young | 4 | 0.83 / 0.05 | 0.14 / 0.07 | 2.77 / 0.31 | 3.22 / 0.22 |
| young | 6 | 0.82 / 0.07 | 0.3 / 0.1 | 3.1 / 0.5 | 4.47 / 0.52 |
#colorListResult %>% ungroup %>% group_by(age,condition) %>% summarise(round(max(k),2)) %>% kable(caption = "Überblick Versuchsreihe Colors k Wert",align = "lcr")
#colorListResult %>% ungroup %>% group_by(age,condition) %>% summarise(round(max(kPashler),2)) %>% kable(caption = "Überblick Versuchsreihe Colors kPashler Wert",align = "lcr")
#objectResult %>% ungroup %>% group_by(age,setsize) %>% summarise(round(max(k),2)) %>% kable(caption = "Überblick Versuchsreihe Real world objects k Wert",align = "lcr")
#objectResult %>% ungroup %>% group_by(age,setsize) %>% summarise(round(max(kPashler),2)) %>% kable(caption = "Überblick Versuchsreihe Real world objects kPashler Wert",align = "lcr")
ggplot(df_KStat_data, aes(x=factor(setsize), y=mean_k, fill=factor(age))) + geom_bar(position=position_dodge(), stat="identity",colour="black",size=.3) +geom_errorbar(aes(ymin=mean_k-sd_k, ymax=mean_k+sd_k),size=.3,width=.2,position=position_dodge(.9))+xlab("Set Size") +ylab("Mean (k)") + scale_fill_hue(name="Age Group") + ggtitle("Color Test K value results per set size")
ggplot(df_O_KStat_data, aes(x=factor(setsize), y=mean_k, fill=factor(age))) + geom_bar(position=position_dodge(), stat="identity",colour="black",size=.3) +geom_errorbar(aes(ymin=mean_k-sd_k, ymax=mean_k+sd_k),size=.3,width=.2,position=position_dodge(.9))+xlab("Set Size") +ylab("Mean (k)") + scale_fill_hue(name="Age Group") + ggtitle("Real World Objects Test K value results per set size")
ggplot(df_KStat_data, aes(x=factor(setsize), y=mean_kPashler, fill=factor(age))) + geom_bar(position=position_dodge(), stat="identity",colour="black",size=.3) +geom_errorbar(aes(ymin=mean_kPashler-sd_kPashler, ymax=mean_kPashler+sd_kPashler),size=.3,width=.2,position=position_dodge(.9))+xlab("Set Size") +ylab("Mean (k-Pashler)") + scale_fill_hue(name="Age Group") + ggtitle("Color Test k-Pashler value results per set size")
ggplot(df_O_KStat_data, aes(x=factor(setsize), y=mean_kPashler, fill=factor(age))) + geom_bar(position=position_dodge(), stat="identity",colour="black",size=.3) +geom_errorbar(aes(ymin=mean_kPashler-sd_kPashler, ymax=mean_kPashler+sd_kPashler),size=.3,width=.2,position=position_dodge(.9))+xlab("Set Size") +ylab("Mean (k-Pashler)") + scale_fill_hue(name="Age Group") + ggtitle("Real World Objects Test k-Pashler value results per set size")
Grafiken Für K-Werte
colorListResult %>% ggplot(mapping=aes(x=age, y=k,fill=age)) + geom_violin() + geom_jitter(width = 0.2, alpha = 0.6)+labs(title="Colors",subtitle = "distribution test persons mean value of k", x="age", y="mean k", fill="Answer")
objectResult %>% ggplot(mapping=aes(x=age, y=k,fill=age)) + geom_violin() + geom_jitter(width = 0.2, alpha = 0.6)+labs(title="Real world objects",subtitle = "distribution test persons mean value of k", x="age", y="mean k", fill="Answer")
colorListResult %>% ggplot(mapping=aes(x=age, y=k,fill=age))+ geom_violin() + geom_jitter(width = 0.2, alpha = 0.6)+facet_wrap(.~condition,scales = 'free')+labs(title="Colors",subtitle = "distribution test persons mean value of k", x="age", y="mean k", fill="Answer")
objectResult %>% ggplot(mapping=aes(x=age, y=k,fill=age))+ geom_violin() + geom_jitter(width = 0.2, alpha = 0.6)+facet_wrap(.~setsize,scales = 'free')+labs(title="Real world objects",subtitle = "distribution test persons mean value of k", x="age", y="mean k", fill="Answer")
colorListResult %>% ggplot(mapping=aes(x=age, y=k,fill=age)) + geom_boxplot() + geom_jitter(width = 0.2, alpha = 0.6)+labs(title="Colors",subtitle = "distribution test persons mean value of k", x="age", y="mean k", fill="Answer")
objectResult %>% ggplot(mapping=aes(x=age, y=k,fill=age)) + geom_boxplot() + geom_jitter(width = 0.2, alpha = 0.6)+labs(title="Real world objects",subtitle = "distribution test persons mean value of k", x="age", y="mean k", fill="Answer")
colorListResult %>% ggplot(mapping=aes(x=age, y=k,fill=age))+ geom_boxplot() + geom_jitter(width = 0.2, alpha = 0.6)+facet_wrap(.~condition,scales = 'free')+labs(title="Colors",subtitle = "distribution test persons mean value of k", x="age", y="mean k", fill="Answer")
objectResult %>% ggplot(mapping=aes(x=age, y=k,fill=age))+ geom_boxplot() + geom_jitter(width = 0.2, alpha = 0.6)+facet_wrap(.~setsize,scales = 'free')+labs(title="Real world objects",subtitle = "distribution test persons mean value of k", x="age", y="mean k", fill="Answer")
####### K Barcharts ##########
mkChart <- ggplot(colorListResult, aes(x = condition,y = k, col = factor(age), fill = factor(age)))
Unterschied zwischen Colors & Objekte H1: Änderungen von Real World Objekten werden von jungen Personen besser erkannt als Änderungen von Farben. H1: Änderungen von Real World Objekten werden von alten Personen besser erkannt als Änderungen von Farben. Notwendige Auswertungen: T-Test abhängige Stichprobe je alt und jung
Voraussetzungen:
Abhängige Variable ist intervallskaliert - ok Es liegen zwei verbundene Stichproben vor aber die Meßwertpaare sind unabhängig - ok Die Unterschiede zwischen den verbundenen Testwerten sind in der Grundgesamtheit normalverteilt.
colorList %>% filter(condition <= 6) %>% summarise(mean(answer_correct)) -> df_mean_color_all